Tracing and fault injection make multi-agent software workflows easier to inspect

The authors combine distributed tracing with targeted failure injection to compare normal and faulty agent runs.

PaperIndustrycs.SEarXiv:2608.24271v1
Zahra Seyedghorban · Egor Klimov · Arie van Deursen · Annibale Panichella · Burcu Kulahcioglu Ozkan

Delft University of Technology · JetBrains Research

Research Digest··2 min read
The authors present llmmas-otel, a framework-agnostic tool for observing and deliberately disrupting LLM-based multi-agent software engineering systems. It records aligned traces across agents, tools, messages, and model calls, enabling reproducible comparisons between baseline and fault-injected executions.

What they did

The authors built llmmas-otel on OpenTelemetry, a standard for collecting distributed-system telemetry. Its instrumentation follows workflow phases, individual agent steps, inter-agent communication, tool calls, and LLM invocations while preserving their relationships in a shared trace.

The tool can inject targeted faults at selected interaction points and retain aligned traces and run artifacts for comparison. The authors performed an initial validation using both a minimal demonstration workflow and an existing LLM-based multi-agent system for software development.

Key findings

  • llmmas-otel captures multiple layers of an agent workflow within trace-aligned telemetry, linking high-level phases to agent actions, messages, tool use, and model calls.
  • Faults can be introduced at chosen interaction points, allowing controlled comparison of baseline and faulty runs rather than relying on naturally occurring failures.
  • The approach is framework-agnostic and was demonstrated on both a small example and a real software-development multi-agent system.
  • The paper establishes feasibility but does not report quantitative improvements in debugging time, fault-detection accuracy, or system reliability.

Why it matters

Multi-agent failures can propagate across model calls, tools, and agent-to-agent messages, making their origin difficult to identify from final outputs alone. Combining structured traces with controlled fault injection provides a practical basis for debugging these systems and for evaluating how reliably they detect, contain, or recover from failures.

Caveats

The evidence is an initial validation on two workflows, not a broad evaluation across agent frameworks, software tasks, or fault classes. The abstract also provides no quantitative assessment of instrumentation overhead, fault coverage, reproducibility across stochastic runs, or whether the traces measurably help developers diagnose failures.

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